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Tree taper has been of interest for over a century, yet questions remain regarding the effects of silvicultural treatments and forest health on recoverable volume. This work utilizes data from Douglas-fir ( Pseudotsuga menziesii (Mirb.)) ( n = 608) and red alder ( Alnus rubra (Bong.)) ( n = 495) trees to assess the influences of fertilization, pruning, thinning, regeneration origin, and defoliation caused by Swiss Needle Cast (SNC; Nothophaeocryptopus gaeumannii), on stem taper in the Pacific Northwest. The Kozak (2004; For. Chor. 80: 507–515) variable-exponent equation was used to test the addition of treatment and crown variables as the model is widely regarded for its flexibility in application. Using a mixed effects framework, results reveal that thinning of Douglas-fir can result in a 3.5% increase in upper stem diameter inside bark, while pruning may lead to a 4.1% decrease. SNC-induced defoliation of Douglas-fir reduced mean diameter above-breast height by 11.5%. Total volume of artificially regenerated red alder was 16% greater than naturally regenerated stems. Overall, thinning of healthy Douglas-fir and planting red alder may increase recoverable volume and C captured in long-term timber products in the region, and the inclusion of crown variables can increase the predictive power of taper estimates for some species. 

Deep learning approaches have been adopted in Forestry research including tree classification and inventory prediction. In this study, we proposed an application of a deep learning approach, Temporal Convolution Network, on sequences of radial resistograph profiles to identify non-thrive trees and to predict wood density. Non-destructive resistance drilling measurements on South and West orientations of 274 trees in a 41-year-old Douglas-fir stand in Marion County, Oregon, USA were used as input series. Non-thrive trees were defined based on their changes in social status since establishment. Wood density was derived by X-ray densitometry from cores obtained by increment borers. Data was split for cross validation. Optimal models were fine-tuned with training and validation datasets, then run with test datasets for model evaluation metrics. Results confirmed that the application of the Temporal Convolution Network on resistograph profiles enables non-thrive tree identification with the probability, represented by the area under the Receiver Operator Characteristic curve, equal to 0.823. Temporal Convolution Network for wood density prediction showed a slight improvement in accuracy (RMSE = 18.22) compared to the traditional linear (RMSE = 20.15) and non-linear (RMSE = 20.33) regression methods. We suggest that the use of machine learning algorithms can be a promising methodology for the analysis of sequential data from non-destructive devices.